This paper studies the application of the DDPG algorithm in trajectory-tracking tasks and proposes a trajectorytracking control method combined with Frenet coordinate system. By converting the vehicle's position and velocity information from the Cartesian coordinate system to Frenet coordinate system, this method can more accurately describe the vehicle's deviation and travel distance relative to the center line of the road. The DDPG algorithm adopts the Actor-Critic framework, uses deep neural networks for strategy and value evaluation, and combines the experience replay mechanism and target network to improve the algorithm's stability and data utilization efficiency. Experimental results show that the DDPG algorithm based on Frenet coordinate system performs well in trajectory-tracking tasks in complex environments, achieves high-precision and stable path tracking, and demonstrates its application potential in autonomous driving and intelligent transportation systems. Keywords- DDPG; path tracking; robot navigation

翻译：本文研究了DDPG算法在轨迹跟踪任务中的应用，提出了一种结合Frenet坐标系的轨迹跟踪控制方法。通过将车辆的位置和速度信息从笛卡尔坐标系转换到Frenet坐标系，该方法能够更精确地描述车辆相对于道路中心线的横向偏差与纵向行驶距离。DDPG算法采用Actor-Critic框架，利用深度神经网络进行策略评估与价值评估，并结合经验回放机制与目标网络以提高算法的稳定性与数据利用效率。实验结果表明，基于Frenet坐标系的DDPG算法在复杂环境下的轨迹跟踪任务中表现优异，实现了高精度且稳定的路径跟踪，展现了其在自动驾驶与智能交通系统中的应用潜力。关键词：DDPG；路径跟踪；机器人导航